Binge Drinking – A Problem?
German crime statistics: violent crime increasingly committed under the influence of alcohol
Adolescents especially affected
Research Questions
Similar to Marcus and Siedler, 2015:
What explains regional differences? Correlation with economic situation?
Night sales ban on alcohol: any effect on binge drinking in Baden-Wuerttemberg?
| Variables | Reasoning |
|---|---|
| F10.0 cases per 1000 | Number of people hospitalized with acute alcohol intoxication |
| K70 cases per 1000 | Number of people hospitalized diagnosed with alcohol-related liver disease |
| GDP per capita | Value and change between years as indicator of economic development |
| (Youth) unemployment rate | Indicator of economic situation for age groups |
| Population density | Indicator for urbanization |
| Treatment dummies (local & temporal) | Capture the policy measure effect |
Question 1: Differences between states
Method: Multiple regression
Question 2: Effect of night-sale-ban
Method: Difference-in-Difference
F10.0 Diagnoses for 15-19 year olds (control: 13 non-city states)
| Dependent variable: | |
| F10.0 cases per 1000 PP | |
| BW night sale ban | -0.06*** |
| (0.01) | |
| Youth unemployment rate | 0.02*** |
| (0.002) | |
| GDP per capita | 0.01*** |
| (0.003) | |
| Observations | 195 |
| Adjusted R2 | 0.42 |
| Note: | p<0.1; p<0.05; p<0.01 |
Marcus and Sielder (2014) find about 7% decrease, we find about 20% decrease
Problems of our research design:
| Dependent variable: | |||
| F10.0 Diagnoses per 1000 capita | |||
| 2000 | 2007 | 2014 | |
| GDP per capita | -0.02** | -0.01 | -0.01 |
| (0.01) | (0.01) | (0.01) | |
| Unemployment rate | -0.04*** | -0.02 | -0.03 |
| (0.01) | (0.03) | (0.04) | |
| Beer tax | 0.0001* | -0.0002 | -0.0002* |
| (0.0001) | (0.0001) | (0.0001) | |
| Population density | 1.63*** | 1.95*** | 2.23*** |
| (0.31) | (0.58) | (0.59) | |
| Observations | 16 | 16 | 16 |
| Adjusted R2 | 0.36 | 0.40 | 0.48 |
| Residual Std. Error (df = 12) | 0.15 | 0.28 | 0.28 |
| Note: | p<0.1; p<0.05; p<0.01 | ||
| Dependent variable: | |||
| Change in F10.0 | Change in F10.2 | Change in K70 | |
| GDP Change | 0.02*** | -0.01 | 0.002 |
| (0.01) | (0.01) | (0.002) | |
| Unemployment Change | -0.01 | -0.03** | 0.002 |
| (0.01) | (0.01) | (0.003) | |
| Observations | 224 | 224 | 224 |
| Adjusted R2 | 0.08 | 0.02 | -0.005 |
| Residual Std. Error (df = 222) | 0.11 | 0.18 | 0.04 |
| Note: | p<0.1; p<0.05; p<0.01 | ||
| Dependent variable: | ||
| F10.0 cases per 1000 PP | ||
| All States | All but City States | |
| BW night sale ban | 0.12 | 0.04 |
| (0.12) | (0.11) | |
| Post-treatment dummy | 0.43*** | 0.48*** |
| (0.05) | (0.05) | |
| Interaction | -0.11 | -0.16 |
| (0.21) | (0.18) | |
| (Intercept) | 0.96*** | 1.04*** |
| (0.03) | (0.03) | |
| Observations | 255 | 210 |
| Adjusted R2 | 0.22 | 0.31 |
| Residual Std. Error | 0.37 (df = 251) | 0.32 (df = 206) |
| Note: | p<0.1; p<0.05; p<0.01 | |
| Dependent variable: | |
| F10.0 cases per 1000 | |
| BW night sale ban | -0.16 |
| (0.11) | |
| Post-sales ban dummy | 0.32*** |
| (0.05) | |
| GDP per capita | -0.02*** |
| (0.003) | |
| Youth unemployment rate | -0.06*** |
| (0.01) | |
| Interaction dummy | 0.04 |
| (0.18) | |
| (Intercept) | 2.30*** |
| (0.13) | |
| Observations | 240 |
| Adjusted R2 | 0.45 |
| Residual Std. Error | 0.32 (df = 234) |
| Note: | p<0.1; p<0.05; p<0.01 |
| Dependent variable: | |||
| F10.0 cases per 1000 PP | |||
| All States | 15-19y | 15-19y + control: non-city | |
| BW night sale ban | -0.06 | -0.03* | -0.06*** |
| (0.09) | (0.02) | (0.01) | |
| Youth unemployment rate | -0.02** | 0.02*** | 0.02*** |
| (0.01) | (0.002) | (0.002) | |
| GDP per capita | -0.01 | 0.003 | 0.01*** |
| (0.02) | (0.003) | (0.003) | |
| Observations | 240 | 240 | 195 |
| Adjusted R2 | 0.02 | 0.29 | 0.42 |
| Note: | p<0.1; p<0.05; p<0.01 | ||